.png){kind=icon}
Return
Electrostatic Layer Jamming Variable Stiffness for Soft Robotics
.png){kind=icon}
PRE
AI
DOI：10.1109/TMECH.2019.2893480.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A novel layer jamming variable stiffness technique for soft robotics is proposed in this paper, which we call electrostatic layer jamming (ELJ). The basic principle of the ELJ is using electrostatic attraction to squeeze material layers to generate friction and then engage jamming. Based on this technique, several specimens used in two common application scenarios including variable tensile stiffness and variable bending stiffness are fabricated, and their stiffness adjustment characteristics are investigated experimentally. Surprisingly, the test data are much larger than the theoretical prediction, which we think is because of the formation of local low air pressure regions between the contact surfaces. Also, the experimental results show that the ELJ technique possesses a large capability of stiffness changing and is space saving. The potential values of the ELJ have been shown by performing with a soft linear actuator for three representative practical applications in the soft robotic field. Finally, the existing problems and advantages of the ELJ technique are discussed, and we believe that this technique will inspire new ways and new opportunities for the soft robotic community.
Keywords:
Electrostatic layer jamming (ELJ)
jamming technology
soft robotics
variable stiffness
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
7.3
Papers:
5.4K
Citations:
2.4W
Organization
Cited Papers
ShareSave
ShareSaveSoft robotics: Technologies and systems pushing the boundaries of robot abilities.png)
SCIENCE ROBOTICS
SCIENCE ROBOTICSIF27.5
PREAI
ShareSaveShareSaveA Novel Soft Machine Table for Manipulation of Delicate Objects Inspired by Caterpillar Locomotion
PREAI
ShareSaveShareSaveShareSave
View more